Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations2104
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory587.9 B

Variable types

Numeric18
DateTime2
Text3
Categorical1

Alerts

clock is highly overall correlated with hw_ncores and 1 other fieldsHigh correlation
code_name_id is highly overall correlated with idHigh correlation
hw_ncores is highly overall correlated with clock and 1 other fieldsHigh correlation
id is highly overall correlated with code_name_idHigh correlation
manufacturer_id is highly overall correlated with technology_idHigh correlation
tdp is highly overall correlated with clockHigh correlation
technology_id is highly overall correlated with manufacturer_idHigh correlation
transistors is highly overall correlated with hw_ncoresHigh correlation
vdd_high is highly overall correlated with vdd_lowHigh correlation
vdd_low is highly overall correlated with vdd_highHigh correlation
hw_nthreadspercore is highly imbalanced (57.6%)Imbalance
vdd_high is highly skewed (γ1 = 45.84222453)Skewed
id is uniformly distributedUniform
id has unique valuesUnique
created_at has unique valuesUnique
updated_at has unique valuesUnique

Reproduction

Analysis started2024-10-15 23:19:43.070384
Analysis finished2024-10-15 23:20:15.580556
Duration32.51 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct2104
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1052.5
Minimum1
Maximum2104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-10-15T23:20:15.643608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile106.15
Q1526.75
median1052.5
Q31578.25
95-th percentile1998.85
Maximum2104
Range2103
Interquartile range (IQR)1051.5

Descriptive statistics

Standard deviation607.5168
Coefficient of variation (CV)0.57721312
Kurtosis-1.2
Mean1052.5
Median Absolute Deviation (MAD)526
Skewness0
Sum2214460
Variance369076.67
MonotonicityStrictly increasing
2024-10-15T23:20:15.761807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
1414 1
 
< 0.1%
1412 1
 
< 0.1%
1411 1
 
< 0.1%
1410 1
 
< 0.1%
1409 1
 
< 0.1%
1408 1
 
< 0.1%
1407 1
 
< 0.1%
1406 1
 
< 0.1%
1405 1
 
< 0.1%
Other values (2094) 2094
99.5%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
2104 1
< 0.1%
2103 1
< 0.1%
2102 1
< 0.1%
2101 1
< 0.1%
2100 1
< 0.1%
2099 1
< 0.1%
2098 1
< 0.1%
2097 1
< 0.1%
2096 1
< 0.1%
2095 1
< 0.1%

created_at
Date

UNIQUE 

Distinct2104
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
Minimum2014-11-17 00:01:26.708453
Maximum2014-11-17 00:02:41.029408
2024-10-15T23:20:15.873125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:16.171263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

updated_at
Date

UNIQUE 

Distinct2104
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
Minimum2014-11-17 00:01:26.708453
Maximum2014-11-17 00:09:11.729122
2024-10-15T23:20:16.299039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:16.422945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

manufacturer_id
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6173954
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-10-15T23:20:16.527094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q19
median9
Q39
95-th percentile15.95
Maximum28
Range27
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.4900172
Coefficient of variation (CV)0.40499676
Kurtosis6.7859323
Mean8.6173954
Median Absolute Deviation (MAD)0
Skewness1.0946825
Sum18131
Variance12.18022
MonotonicityNot monotonic
2024-10-15T23:20:16.631816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
9 1643
78.1%
1 148
 
7.0%
7 67
 
3.2%
3 56
 
2.7%
19 42
 
2.0%
6 41
 
1.9%
4 31
 
1.5%
17 30
 
1.4%
10 10
 
0.5%
18 7
 
0.3%
Other values (11) 29
 
1.4%
ValueCountFrequency (%)
1 148
 
7.0%
2 1
 
< 0.1%
3 56
 
2.7%
4 31
 
1.5%
5 1
 
< 0.1%
6 41
 
1.9%
7 67
 
3.2%
9 1643
78.1%
10 10
 
0.5%
17 30
 
1.4%
ValueCountFrequency (%)
28 4
 
0.2%
27 2
 
0.1%
26 3
 
0.1%
25 4
 
0.2%
24 1
 
< 0.1%
23 2
 
0.1%
22 4
 
0.2%
21 6
 
0.3%
20 1
 
< 0.1%
19 42
2.0%

processor_family_id
Real number (ℝ)

Distinct193
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.939163
Minimum1
Maximum193
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-10-15T23:20:16.745886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median18
Q340
95-th percentile172.85
Maximum193
Range192
Interquartile range (IQR)36

Descriptive statistics

Standard deviation50.912296
Coefficient of variation (CV)1.3782742
Kurtosis2.0793665
Mean36.939163
Median Absolute Deviation (MAD)14
Skewness1.7964239
Sum77720
Variance2592.0619
MonotonicityNot monotonic
2024-10-15T23:20:16.875283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 393
18.7%
4 163
 
7.7%
20 131
 
6.2%
14 127
 
6.0%
22 124
 
5.9%
8 85
 
4.0%
24 74
 
3.5%
18 73
 
3.5%
40 57
 
2.7%
10 49
 
2.3%
Other values (183) 828
39.4%
ValueCountFrequency (%)
1 393
18.7%
2 27
 
1.3%
3 19
 
0.9%
4 163
7.7%
5 22
 
1.0%
6 13
 
0.6%
7 7
 
0.3%
8 85
 
4.0%
9 19
 
0.9%
10 49
 
2.3%
ValueCountFrequency (%)
193 1
 
< 0.1%
192 7
 
0.3%
191 11
 
0.5%
190 1
 
< 0.1%
189 1
 
< 0.1%
188 1
 
< 0.1%
187 1
 
< 0.1%
186 8
 
0.4%
185 1
 
< 0.1%
184 42
2.0%

microarchitecture_id
Real number (ℝ)

Distinct79
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.307985
Minimum1
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-10-15T23:20:17.003508image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q136.307985
median36.307985
Q336.307985
95-th percentile51
Maximum85
Range84
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.865978
Coefficient of variation (CV)0.2992724
Kurtosis6.4695334
Mean36.307985
Median Absolute Deviation (MAD)0
Skewness0.34926991
Sum76392
Variance118.06948
MonotonicityNot monotonic
2024-10-15T23:20:17.122078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.30798479 1578
75.0%
42 75
 
3.6%
7 51
 
2.4%
36 37
 
1.8%
32 26
 
1.2%
6 23
 
1.1%
51 20
 
1.0%
35 17
 
0.8%
1 14
 
0.7%
50 14
 
0.7%
Other values (69) 249
 
11.8%
ValueCountFrequency (%)
1 14
 
0.7%
2 2
 
0.1%
3 2
 
0.1%
4 3
 
0.1%
5 1
 
< 0.1%
6 23
1.1%
7 51
2.4%
8 7
 
0.3%
9 6
 
0.3%
10 4
 
0.2%
ValueCountFrequency (%)
85 4
 
0.2%
84 1
 
< 0.1%
83 1
 
< 0.1%
82 1
 
< 0.1%
81 1
 
< 0.1%
80 8
0.4%
79 2
 
0.1%
78 1
 
< 0.1%
77 13
0.6%
76 1
 
< 0.1%

code_name_id
Real number (ℝ)

HIGH CORRELATION 

Distinct206
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.769032
Minimum1
Maximum205
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-10-15T23:20:17.243160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q133
median56
Q3114
95-th percentile192
Maximum205
Range204
Interquartile range (IQR)81

Descriptive statistics

Standard deviation61.219152
Coefficient of variation (CV)0.79744593
Kurtosis-0.67930741
Mean76.769032
Median Absolute Deviation (MAD)30
Skewness0.80153824
Sum161522.04
Variance3747.7846
MonotonicityNot monotonic
2024-10-15T23:20:17.373654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
182 177
 
8.4%
76.76903159 173
 
8.2%
54 125
 
5.9%
64 120
 
5.7%
3 69
 
3.3%
36 67
 
3.2%
14 56
 
2.7%
7 47
 
2.2%
10 44
 
2.1%
29 41
 
1.9%
Other values (196) 1185
56.3%
ValueCountFrequency (%)
1 9
 
0.4%
2 2
 
0.1%
3 69
3.3%
4 8
 
0.4%
5 15
 
0.7%
6 24
 
1.1%
7 47
2.2%
8 16
 
0.8%
9 9
 
0.4%
10 44
2.1%
ValueCountFrequency (%)
205 1
 
< 0.1%
204 7
 
0.3%
203 3
 
0.1%
202 4
 
0.2%
201 2
 
0.1%
200 3
 
0.1%
199 28
1.3%
198 9
 
0.4%
197 3
 
0.1%
196 2
 
0.1%

technology_id
Real number (ℝ)

HIGH CORRELATION 

Distinct103
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.459816
Minimum1
Maximum110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-10-15T23:20:17.503009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q148.459816
median48.459816
Q348.459816
95-th percentile67
Maximum110
Range109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation17.370464
Coefficient of variation (CV)0.35845089
Kurtosis3.3302543
Mean48.459816
Median Absolute Deviation (MAD)0
Skewness-0.24051634
Sum101959.45
Variance301.73301
MonotonicityNot monotonic
2024-10-15T23:20:17.625878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48.45981555 1345
63.9%
61 124
 
5.9%
62 78
 
3.7%
3 41
 
1.9%
60 32
 
1.5%
4 28
 
1.3%
66 26
 
1.2%
5 26
 
1.2%
2 20
 
1.0%
65 19
 
0.9%
Other values (93) 365
 
17.3%
ValueCountFrequency (%)
1 14
 
0.7%
2 20
1.0%
3 41
1.9%
4 28
1.3%
5 26
1.2%
6 4
 
0.2%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 3
 
0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
110 1
 
< 0.1%
109 4
0.2%
108 6
0.3%
107 2
 
0.1%
106 1
 
< 0.1%
105 1
 
< 0.1%
104 4
0.2%
103 9
0.4%
102 2
 
0.1%
101 1
 
< 0.1%

cache_on_id
Real number (ℝ)

Distinct128
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.139259
Minimum1
Maximum139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-10-15T23:20:17.741425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q341.25
95-th percentile105.85
Maximum139
Range138
Interquartile range (IQR)39.25

Descriptive statistics

Standard deviation36.910181
Coefficient of variation (CV)1.3600291
Kurtosis0.5113525
Mean27.139259
Median Absolute Deviation (MAD)3
Skewness1.3553377
Sum57101
Variance1362.3615
MonotonicityNot monotonic
2024-10-15T23:20:17.865808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 745
35.4%
1 143
 
6.8%
3 78
 
3.7%
5 76
 
3.6%
4 67
 
3.2%
48 51
 
2.4%
6 46
 
2.2%
7 39
 
1.9%
39 36
 
1.7%
102 34
 
1.6%
Other values (118) 789
37.5%
ValueCountFrequency (%)
1 143
 
6.8%
2 745
35.4%
3 78
 
3.7%
4 67
 
3.2%
5 76
 
3.6%
6 46
 
2.2%
7 39
 
1.9%
8 19
 
0.9%
9 4
 
0.2%
10 22
 
1.0%
ValueCountFrequency (%)
139 4
0.2%
138 2
 
0.1%
137 2
 
0.1%
136 1
 
< 0.1%
135 1
 
< 0.1%
134 1
 
< 0.1%
133 1
 
< 0.1%
132 1
 
< 0.1%
131 1
 
< 0.1%
130 6
0.3%

cache_off_id
Real number (ℝ)

Distinct24
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5042776
Minimum1
Maximum129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-10-15T23:20:17.994788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median2
Q32
95-th percentile7
Maximum129
Range128
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.9317154
Coefficient of variation (CV)2.5488036
Kurtosis61.035413
Mean3.5042776
Median Absolute Deviation (MAD)0
Skewness7.4881483
Sum7373
Variance79.77554
MonotonicityNot monotonic
2024-10-15T23:20:18.122341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2 1915
91.0%
7 38
 
1.8%
3 25
 
1.2%
8 23
 
1.1%
1 20
 
1.0%
5 16
 
0.8%
4 11
 
0.5%
27 9
 
0.4%
63 8
 
0.4%
20 6
 
0.3%
Other values (14) 33
 
1.6%
ValueCountFrequency (%)
1 20
 
1.0%
2 1915
91.0%
3 25
 
1.2%
4 11
 
0.5%
5 16
 
0.8%
7 38
 
1.8%
8 23
 
1.1%
9 2
 
0.1%
20 6
 
0.3%
27 9
 
0.4%
ValueCountFrequency (%)
129 1
 
< 0.1%
79 6
0.3%
75 1
 
< 0.1%
74 2
 
0.1%
73 1
 
< 0.1%
69 3
0.1%
68 1
 
< 0.1%
67 1
 
< 0.1%
66 4
0.2%
64 3
0.1%

die_photo_id
Real number (ℝ)

Distinct59
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.723183
Minimum1
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-10-15T23:20:18.247105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile36.723183
Q136.723183
median36.723183
Q336.723183
95-th percentile46
Maximum60
Range59
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.8168208
Coefficient of variation (CV)0.15839642
Kurtosis13.610293
Mean36.723183
Median Absolute Deviation (MAD)0
Skewness-2.1602769
Sum77265.578
Variance33.835404
MonotonicityNot monotonic
2024-10-15T23:20:18.392103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.72318339 1815
86.3%
44 30
 
1.4%
46 26
 
1.2%
47 15
 
0.7%
39 14
 
0.7%
48 13
 
0.6%
42 13
 
0.6%
52 13
 
0.6%
10 12
 
0.6%
51 11
 
0.5%
Other values (49) 142
 
6.7%
ValueCountFrequency (%)
1 2
 
0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 7
0.3%
8 5
0.2%
9 6
0.3%
10 12
0.6%
ValueCountFrequency (%)
60 1
 
< 0.1%
59 5
 
0.2%
58 4
 
0.2%
57 1
 
< 0.1%
56 2
 
0.1%
55 1
 
< 0.1%
54 1
 
< 0.1%
53 5
 
0.2%
52 13
0.6%
51 11
0.5%

model
Text

Distinct1360
Distinct (%)64.6%
Missing0
Missing (%)0.0%
Memory size132.9 KiB
2024-10-15T23:20:18.705721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length7.6397338
Min length3

Characters and Unicode

Total characters16074
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1205 ?
Unique (%)57.3%

Sample

1st row3.60E
2nd row3.6
3rd row3.80E
4th rowunknown model
5th row310
ValueCountFrequency (%)
unknown 555
 
20.0%
model 555
 
20.0%
v2 69
 
2.5%
v3 48
 
1.7%
r4400sc 4
 
0.1%
330 4
 
0.1%
350 4
 
0.1%
750 4
 
0.1%
e5-1660 3
 
0.1%
2600 3
 
0.1%
Other values (1277) 1530
55.1%
2024-10-15T23:20:19.120790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 1665
 
10.4%
0 1545
 
9.6%
o 1110
 
6.9%
5 917
 
5.7%
3 756
 
4.7%
2 695
 
4.3%
675
 
4.2%
7 575
 
3.6%
m 557
 
3.5%
u 555
 
3.5%
Other values (39) 7024
43.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16074
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1665
 
10.4%
0 1545
 
9.6%
o 1110
 
6.9%
5 917
 
5.7%
3 756
 
4.7%
2 695
 
4.3%
675
 
4.2%
7 575
 
3.6%
m 557
 
3.5%
u 555
 
3.5%
Other values (39) 7024
43.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16074
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1665
 
10.4%
0 1545
 
9.6%
o 1110
 
6.9%
5 917
 
5.7%
3 756
 
4.7%
2 695
 
4.3%
675
 
4.2%
7 575
 
3.6%
m 557
 
3.5%
u 555
 
3.5%
Other values (39) 7024
43.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16074
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1665
 
10.4%
0 1545
 
9.6%
o 1110
 
6.9%
5 917
 
5.7%
3 756
 
4.7%
2 695
 
4.3%
675
 
4.2%
7 575
 
3.6%
m 557
 
3.5%
u 555
 
3.5%
Other values (39) 7024
43.7%

date
Text

Distinct219
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Memory size139.2 KiB
2024-10-15T23:20:19.354044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.680608
Min length10

Characters and Unicode

Total characters22472
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique110 ?
Unique (%)5.2%

Sample

1st rowunknown date
2nd rowunknown date
3rd rowunknown date
4th rowunknown date
5th rowunknown date
ValueCountFrequency (%)
unknown 716
25.4%
date 716
25.4%
2013-01-07 88
 
3.1%
2012-01-04 70
 
2.5%
2014-01-01 63
 
2.2%
2010-01-01 61
 
2.2%
2014-01-04 59
 
2.1%
2014-01-07 53
 
1.9%
2013-01-04 50
 
1.8%
2011-01-04 50
 
1.8%
Other values (210) 894
31.7%
2024-10-15T23:20:19.722023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4449
19.8%
1 3051
13.6%
- 2776
12.4%
n 2148
9.6%
2 1424
 
6.3%
t 716
 
3.2%
e 716
 
3.2%
u 716
 
3.2%
a 716
 
3.2%
d 716
 
3.2%
Other values (11) 5044
22.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22472
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4449
19.8%
1 3051
13.6%
- 2776
12.4%
n 2148
9.6%
2 1424
 
6.3%
t 716
 
3.2%
e 716
 
3.2%
u 716
 
3.2%
a 716
 
3.2%
d 716
 
3.2%
Other values (11) 5044
22.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22472
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4449
19.8%
1 3051
13.6%
- 2776
12.4%
n 2148
9.6%
2 1424
 
6.3%
t 716
 
3.2%
e 716
 
3.2%
u 716
 
3.2%
a 716
 
3.2%
d 716
 
3.2%
Other values (11) 5044
22.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22472
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4449
19.8%
1 3051
13.6%
- 2776
12.4%
n 2148
9.6%
2 1424
 
6.3%
t 716
 
3.2%
e 716
 
3.2%
u 716
 
3.2%
a 716
 
3.2%
d 716
 
3.2%
Other values (11) 5044
22.4%

clock
Real number (ℝ)

HIGH CORRELATION 

Distinct244
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1975.6101
Minimum0.108
Maximum21300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-10-15T23:20:19.863748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.108
5-th percentile132.15
Q11300
median2100
Q32762.5
95-th percentile3400
Maximum21300
Range21299.892
Interquartile range (IQR)1462.5

Descriptive statistics

Standard deviation1174.482
Coefficient of variation (CV)0.59449076
Kurtosis68.131371
Mean1975.6101
Median Absolute Deviation (MAD)700
Skewness3.9997965
Sum4156683.7
Variance1379407.9
MonotonicityNot monotonic
2024-10-15T23:20:20.010588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000 97
 
4.6%
2400 87
 
4.1%
2800 84
 
4.0%
1600 82
 
3.9%
3000 70
 
3.3%
3200 66
 
3.1%
2600 60
 
2.9%
2200 52
 
2.5%
2660 48
 
2.3%
2500 47
 
2.2%
Other values (234) 1411
67.1%
ValueCountFrequency (%)
0.108 1
< 0.1%
0.2 1
< 0.1%
1 1
< 0.1%
2 2
0.1%
2.5 1
< 0.1%
3 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
8 2
0.1%
10 2
0.1%
ValueCountFrequency (%)
21300 2
 
0.1%
5000 1
 
< 0.1%
4700 1
 
< 0.1%
4200 1
 
< 0.1%
4100 1
 
< 0.1%
4000 3
 
0.1%
3900 1
 
< 0.1%
3800 9
0.4%
3733 2
 
0.1%
3730 3
 
0.1%

max_clock
Real number (ℝ)

Distinct52
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3197.4343
Minimum1333
Maximum4400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-10-15T23:20:20.141490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1333
5-th percentile2800
Q13197.4343
median3197.4343
Q33197.4343
95-th percentile3700
Maximum4400
Range3067
Interquartile range (IQR)0

Descriptive statistics

Standard deviation280.18565
Coefficient of variation (CV)0.087628274
Kurtosis11.123308
Mean3197.4343
Median Absolute Deviation (MAD)0
Skewness-1.7136618
Sum6727401.7
Variance78503.996
MonotonicityNot monotonic
2024-10-15T23:20:20.261767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3197.434263 1602
76.1%
3600 44
 
2.1%
3200 39
 
1.9%
3800 32
 
1.5%
3300 31
 
1.5%
3500 29
 
1.4%
3900 28
 
1.3%
3700 26
 
1.2%
2800 25
 
1.2%
3400 21
 
1.0%
Other values (42) 227
 
10.8%
ValueCountFrequency (%)
1333 4
0.2%
1466 1
 
< 0.1%
1500 1
 
< 0.1%
1600 1
 
< 0.1%
1730 1
 
< 0.1%
1733 2
0.1%
1800 1
 
< 0.1%
1860 2
0.1%
1866 3
0.1%
1900 2
0.1%
ValueCountFrequency (%)
4400 1
 
< 0.1%
4100 3
 
0.1%
4000 16
 
0.8%
3900 28
1.3%
3870 1
 
< 0.1%
3860 2
 
0.1%
3800 32
1.5%
3730 8
 
0.4%
3700 26
1.2%
3600 44
2.1%

hw_nthreadspercore
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size123.5 KiB
1.0
1349 
2.0
745 
1.362726406101049
 
6
4.0
 
3
8.0
 
1

Length

Max length17
Median length3
Mean length3.039924
Min length3

Characters and Unicode

Total characters6396
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1349
64.1%
2.0 745
35.4%
1.362726406101049 6
 
0.3%
4.0 3
 
0.1%
8.0 1
 
< 0.1%

Length

2024-10-15T23:20:20.376770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-15T23:20:20.474857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1349
64.1%
2.0 745
35.4%
1.362726406101049 6
 
0.3%
4.0 3
 
0.1%
8.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 2116
33.1%
. 2104
32.9%
1 1367
21.4%
2 757
 
11.8%
6 18
 
0.3%
4 15
 
0.2%
3 6
 
0.1%
7 6
 
0.1%
9 6
 
0.1%
8 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6396
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2116
33.1%
. 2104
32.9%
1 1367
21.4%
2 757
 
11.8%
6 18
 
0.3%
4 15
 
0.2%
3 6
 
0.1%
7 6
 
0.1%
9 6
 
0.1%
8 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6396
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2116
33.1%
. 2104
32.9%
1 1367
21.4%
2 757
 
11.8%
6 18
 
0.3%
4 15
 
0.2%
3 6
 
0.1%
7 6
 
0.1%
9 6
 
0.1%
8 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6396
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2116
33.1%
. 2104
32.9%
1 1367
21.4%
2 757
 
11.8%
6 18
 
0.3%
4 15
 
0.2%
3 6
 
0.1%
7 6
 
0.1%
9 6
 
0.1%
8 1
 
< 0.1%

hw_ncores
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7471429
Minimum1
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-10-15T23:20:20.569263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile8
Maximum61
Range60
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.3206442
Coefficient of variation (CV)1.5727774
Kurtosis125.79669
Mean2.7471429
Median Absolute Deviation (MAD)1
Skewness10.027521
Sum5779.9886
Variance18.667966
MonotonicityNot monotonic
2024-10-15T23:20:20.659691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 834
39.6%
2 682
32.4%
4 407
19.3%
6 64
 
3.0%
8 50
 
2.4%
10 23
 
1.1%
12 12
 
0.6%
15 11
 
0.5%
61 4
 
0.2%
2.747142857 4
 
0.2%
Other values (6) 13
 
0.6%
ValueCountFrequency (%)
1 834
39.6%
2 682
32.4%
2.747142857 4
 
0.2%
3 3
 
0.1%
4 407
19.3%
6 64
 
3.0%
8 50
 
2.4%
10 23
 
1.1%
12 12
 
0.6%
14 3
 
0.1%
ValueCountFrequency (%)
61 4
 
0.2%
60 2
 
0.1%
57 3
 
0.1%
18 1
 
< 0.1%
16 1
 
< 0.1%
15 11
 
0.5%
14 3
 
0.1%
12 12
 
0.6%
10 23
1.1%
8 50
2.4%

tdp
Real number (ℝ)

HIGH CORRELATION 

Distinct221
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.355546
Minimum0.5
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-10-15T23:20:20.769762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile7.5
Q131.7
median61.355546
Q384
95-th percentile130
Maximum300
Range299.5
Interquartile range (IQR)52.3

Descriptive statistics

Standard deviation40.027803
Coefficient of variation (CV)0.65239096
Kurtosis3.4152863
Mean61.355546
Median Absolute Deviation (MAD)26.355546
Skewness1.1466395
Sum129092.07
Variance1602.225
MonotonicityNot monotonic
2024-10-15T23:20:20.890879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61.35554551 335
 
15.9%
35 174
 
8.3%
65 134
 
6.4%
95 129
 
6.1%
130 118
 
5.6%
80 71
 
3.4%
84 52
 
2.5%
17 51
 
2.4%
45 40
 
1.9%
73 38
 
1.8%
Other values (211) 962
45.7%
ValueCountFrequency (%)
0.5 1
< 0.1%
0.65 1
< 0.1%
0.72 1
< 0.1%
0.8 2
0.1%
1 1
< 0.1%
1.2 1
< 0.1%
1.3 2
0.1%
1.4 1
< 0.1%
1.5 1
< 0.1%
1.6 1
< 0.1%
ValueCountFrequency (%)
300 6
0.3%
270 1
 
< 0.1%
245 1
 
< 0.1%
225 1
 
< 0.1%
200 2
 
0.1%
185 2
 
0.1%
170 3
 
0.1%
165 4
0.2%
160 6
0.3%
155 8
0.4%

source
Text

Distinct1819
Distinct (%)86.5%
Missing0
Missing (%)0.0%
Memory size282.6 KiB
2024-10-15T23:20:21.042219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length160
Median length147
Mean length80.47576
Min length6

Characters and Unicode

Total characters169321
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1779 ?
Unique (%)84.6%

Sample

1st rowhttp://ark.intel.com/products/27089/64-bit-Intel-Xeon-Processor-3_60E-GHz-2M-Cache-800-MHz-FSB
2nd rowhttp://ark.intel.com/products/28019/64-bit-Intel-Xeon-Processor-3_60-GHz-2M-Cache-800-MHz-FSB
3rd rowhttp://ark.intel.com/products/27092/64-bit-Intel-Xeon-Processor-3_80E-GHz-2M-Cache-800-MHz-FSB
4th rowhttp://ark.intel.com/products/27103/64-bit-Intel-Xeon-Processor-3_66-GHz-1M-Cache-667-MHz-FSB
5th rowhttp://ark.intel.com/products/27104/Intel-Celeron-D-Processor-310-(256K-Cache-2_13-GHz-533-MHz-FSB)
ValueCountFrequency (%)
horowitz 141
 
5.3%
spreadsheet 141
 
5.3%
merged 45
 
1.7%
unknown 34
 
1.3%
source 34
 
1.3%
spec 29
 
1.1%
website 29
 
1.1%
model 18
 
0.7%
pa-risc 8
 
0.3%
informationssysteme 7
 
0.3%
Other values (2033) 2161
81.6%
2024-10-15T23:20:21.516207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 15214
 
9.0%
t 10236
 
6.0%
e 9754
 
5.8%
/ 9521
 
5.6%
o 8704
 
5.1%
r 8309
 
4.9%
c 7151
 
4.2%
0 6787
 
4.0%
s 6370
 
3.8%
p 4903
 
2.9%
Other values (66) 82372
48.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 169321
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 15214
 
9.0%
t 10236
 
6.0%
e 9754
 
5.8%
/ 9521
 
5.6%
o 8704
 
5.1%
r 8309
 
4.9%
c 7151
 
4.2%
0 6787
 
4.0%
s 6370
 
3.8%
p 4903
 
2.9%
Other values (66) 82372
48.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 169321
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 15214
 
9.0%
t 10236
 
6.0%
e 9754
 
5.8%
/ 9521
 
5.6%
o 8704
 
5.1%
r 8309
 
4.9%
c 7151
 
4.2%
0 6787
 
4.0%
s 6370
 
3.8%
p 4903
 
2.9%
Other values (66) 82372
48.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 169321
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 15214
 
9.0%
t 10236
 
6.0%
e 9754
 
5.8%
/ 9521
 
5.6%
o 8704
 
5.1%
r 8309
 
4.9%
c 7151
 
4.2%
0 6787
 
4.0%
s 6370
 
3.8%
p 4903
 
2.9%
Other values (66) 82372
48.6%

bus_width
Real number (ℝ)

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.387569
Minimum4
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-10-15T23:20:21.603355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile32
Q156.387569
median56.387569
Q356.387569
95-th percentile64
Maximum64
Range60
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.4667064
Coefficient of variation (CV)0.1324176
Kurtosis12.514136
Mean56.387569
Median Absolute Deviation (MAD)0
Skewness-3.0827091
Sum118639.44
Variance55.751705
MonotonicityNot monotonic
2024-10-15T23:20:21.680105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
56.38756856 1557
74.0%
64 426
 
20.2%
32 107
 
5.1%
8 7
 
0.3%
16 6
 
0.3%
4 1
 
< 0.1%
ValueCountFrequency (%)
4 1
 
< 0.1%
8 7
 
0.3%
16 6
 
0.3%
32 107
 
5.1%
56.38756856 1557
74.0%
64 426
 
20.2%
ValueCountFrequency (%)
64 426
 
20.2%
56.38756856 1557
74.0%
32 107
 
5.1%
16 6
 
0.3%
8 7
 
0.3%
4 1
 
< 0.1%

transistors
Real number (ℝ)

HIGH CORRELATION 

Distinct140
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean315.57645
Minimum0.0023
Maximum1900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-10-15T23:20:21.783819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0023
5-th percentile5.1
Q1169
median315.57645
Q3315.57645
95-th percentile774
Maximum1900
Range1899.9977
Interquartile range (IQR)146.57645

Descriptive statistics

Standard deviation268.39311
Coefficient of variation (CV)0.85048521
Kurtosis13.119251
Mean315.57645
Median Absolute Deviation (MAD)24.576454
Skewness2.9936393
Sum663972.86
Variance72034.86
MonotonicityNot monotonic
2024-10-15T23:20:21.906686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
315.576454 1008
47.9%
410 91
 
4.3%
291 81
 
3.8%
125 62
 
2.9%
382 60
 
2.9%
151 48
 
2.3%
55 45
 
2.1%
820 38
 
1.8%
731 32
 
1.5%
582 30
 
1.4%
Other values (130) 609
28.9%
ValueCountFrequency (%)
0.0023 1
< 0.1%
0.0035 1
< 0.1%
0.00351 1
< 0.1%
0.006 1
< 0.1%
0.0068 1
< 0.1%
0.0085 1
< 0.1%
0.029 2
0.1%
0.068 2
0.1%
0.08 1
< 0.1%
0.11 1
< 0.1%
ValueCountFrequency (%)
1900 7
 
0.3%
1720 28
1.3%
1400 2
 
0.1%
1328 9
 
0.4%
1200 2
 
0.1%
1170 1
 
< 0.1%
995 1
 
< 0.1%
904 1
 
< 0.1%
820 38
1.8%
790 5
 
0.2%

die_size
Real number (ℝ)

Distinct135
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175.6919
Minimum1
Maximum1215
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-10-15T23:20:22.030993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile81
Q1131
median175.6919
Q3175.6919
95-th percentile299
Maximum1215
Range1214
Interquartile range (IQR)44.691897

Descriptive statistics

Standard deviation87.315447
Coefficient of variation (CV)0.4969805
Kurtosis16.964598
Mean175.6919
Median Absolute Deviation (MAD)20.308103
Skewness2.9463367
Sum369655.75
Variance7623.9873
MonotonicityNot monotonic
2024-10-15T23:20:22.156614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
175.6918967 981
46.6%
107 82
 
3.9%
143 81
 
3.8%
81 78
 
3.7%
90 77
 
3.7%
112 62
 
2.9%
131 45
 
2.1%
263 38
 
1.8%
214 38
 
1.8%
286 30
 
1.4%
Other values (125) 592
28.1%
ValueCountFrequency (%)
1 2
 
0.1%
12 1
 
< 0.1%
16 2
 
0.1%
17 1
 
< 0.1%
18 1
 
< 0.1%
20 1
 
< 0.1%
26 9
0.4%
33 4
0.2%
42 1
 
< 0.1%
44 2
 
0.1%
ValueCountFrequency (%)
1215 1
 
< 0.1%
596 28
1.3%
567 2
 
0.1%
555 4
 
0.2%
503 7
 
0.3%
467 2
 
0.1%
435 9
 
0.4%
432 4
 
0.2%
421 1
 
< 0.1%
397 3
 
0.1%

vdd_low
Real number (ℝ)

HIGH CORRELATION 

Distinct91
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4786762
Minimum0.3
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-10-15T23:20:22.278453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.65375
Q11.075
median1.4786762
Q31.4786762
95-th percentile3.3
Maximum15
Range14.7
Interquartile range (IQR)0.40367622

Descriptive statistics

Standard deviation0.80819287
Coefficient of variation (CV)0.54656514
Kurtosis43.772511
Mean1.4786762
Median Absolute Deviation (MAD)0
Skewness4.4672614
Sum3111.1348
Variance0.65317572
MonotonicityNot monotonic
2024-10-15T23:20:22.399450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.478676218 1057
50.2%
0.85 139
 
6.6%
3.3 69
 
3.3%
1.25 67
 
3.2%
0.65 56
 
2.7%
1.2 54
 
2.6%
0.8 49
 
2.3%
5 47
 
2.2%
0.6 47
 
2.2%
1 43
 
2.0%
Other values (81) 476
22.6%
ValueCountFrequency (%)
0.3 3
 
0.1%
0.6 47
2.2%
0.65 56
2.7%
0.675 7
 
0.3%
0.7 2
 
0.1%
0.75 41
1.9%
0.762 1
 
< 0.1%
0.7625 13
 
0.6%
0.775 5
 
0.2%
0.8 49
2.3%
ValueCountFrequency (%)
15 1
 
< 0.1%
5 47
2.2%
4 1
 
< 0.1%
3.6 3
 
0.1%
3.52 2
 
0.1%
3.45 4
 
0.2%
3.4 1
 
< 0.1%
3.3 69
3.3%
3.2 1
 
< 0.1%
3.135 3
 
0.1%

vdd_high
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct83
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.51261
Minimum0
Maximum13625
Zeros6
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-10-15T23:20:22.523725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.2
Q11.3625
median3.3
Q313.51261
95-th percentile13.51261
Maximum13625
Range13625
Interquartile range (IQR)12.15011

Descriptive statistics

Standard deviation296.94419
Coefficient of variation (CV)21.975339
Kurtosis2102.3385
Mean13.51261
Median Absolute Deviation (MAD)2.1
Skewness45.842225
Sum28430.532
Variance88175.851
MonotonicityNot monotonic
2024-10-15T23:20:22.661725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.51261025 943
44.8%
1.35 143
 
6.8%
1.4 120
 
5.7%
1.3 85
 
4.0%
1.5 83
 
3.9%
3.3 74
 
3.5%
1.3625 60
 
2.9%
1.25 46
 
2.2%
5 44
 
2.1%
1.375 36
 
1.7%
Other values (73) 470
22.3%
ValueCountFrequency (%)
0 6
 
0.3%
0.9 1
 
< 0.1%
0.94 2
 
0.1%
0.956 4
 
0.2%
0.975 2
 
0.1%
1 2
 
0.1%
1.004 5
 
0.2%
1.062 1
 
< 0.1%
1.1 20
1.0%
1.116 2
 
0.1%
ValueCountFrequency (%)
13625 1
 
< 0.1%
15 1
 
< 0.1%
13.51261025 943
44.8%
12 4
 
0.2%
5.25 1
 
< 0.1%
5 44
 
2.1%
4 1
 
< 0.1%
3.6 11
 
0.5%
3.52 2
 
0.1%
3.3 74
 
3.5%

Interactions

2024-10-15T23:20:13.587697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:43.662641image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:45.172638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:46.922752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:48.586197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:50.337221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:52.291308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:53.769458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:55.381857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:57.244694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:59.060942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:00.611466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:02.248002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:04.280666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:05.958277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:07.560451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:09.449938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:11.632251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:13.684614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:43.735745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:45.272075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:47.003679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:48.663730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:50.450538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:52.362613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:53.846664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:55.471187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:57.333715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:59.150335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:00.688376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:02.331379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:04.370802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:06.035360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:07.651649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:09.526921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:11.750294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:13.780300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:43.813718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:45.368458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:47.088464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:48.745232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:50.555439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:52.437149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:53.925451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:55.564188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:57.428197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:59.236047image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:00.770487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:02.477808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:04.485001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:06.112728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:07.744170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:09.617684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:11.851930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:13.876672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:43.906961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:45.490091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:47.180349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:48.842294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:50.693521image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:52.523407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:54.021862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:55.668708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:57.524096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:59.324376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:00.864890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:02.606998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:04.613095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:06.210026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:07.884261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:09.720369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:11.972677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:13.955792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:43.980367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:45.580710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:47.266928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:48.927904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:50.808198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:52.598120image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:54.124025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:55.749909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:57.616990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:59.403319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:00.940792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:02.728386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:04.716007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:06.300228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:08.022574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:09.821320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:12.076514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:14.048606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:44.090327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:45.830583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:47.380233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:49.028999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:50.944021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:52.686829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:54.247132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:55.856207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:57.900108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:59.496644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:01.029766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:02.832902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:04.818710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:06.396371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:08.191893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:09.931006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:12.217634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:14.130037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:44.171980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:45.910306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:47.484838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:49.116660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:51.051919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:52.766700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:54.329760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:55.938094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:57.985164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:59.579470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:01.108665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:02.920175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:04.910198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:06.478198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:08.298043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:10.026837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:12.328317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:14.217675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:44.257857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:45.996920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:47.579946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:49.228983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:51.149661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:52.852563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:54.418091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:56.030062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:58.069771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:59.669013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:01.221956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:03.026496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:05.019361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:06.566655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:08.403651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:10.132748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:12.445374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:14.305352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:44.341055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:46.077234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:47.669060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:49.330769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:51.237139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:52.936556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:54.504088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:56.126350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:58.150024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:59.756845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:01.320538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:03.124568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:05.118335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:06.651215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:08.502375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:10.241201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:12.569207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:14.388808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:44.424995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:46.156803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:47.760212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:49.438492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:51.327001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:53.019332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:54.601143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:56.236908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:58.233834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:59.847059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:01.409975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:03.221665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:05.215570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:06.738332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:08.604530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:10.583704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:12.679520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:14.464813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:44.501399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:46.239613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:47.841002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:49.564252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:51.412570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:53.095974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:54.688035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:56.322243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:58.315169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:59.925913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:01.489949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:03.304798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:05.307601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:06.818078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:08.695404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:10.688184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:12.775105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:14.555881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:44.579582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:46.326464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:47.930356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:49.652993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:51.501958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:53.175569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:54.777472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:56.417039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:58.400525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:00.014640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:01.593743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:03.399472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:05.388097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:06.904899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:08.796059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:10.808375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:12.877392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:14.643577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:44.657837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:46.412308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:48.016026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:49.751672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:51.763962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:53.253837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:54.866229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:56.512386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:58.483195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:00.097837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:01.681845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:03.499872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:05.470737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:07.001734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:08.889712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:10.923745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:12.966038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:14.729703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:44.737945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:46.495880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:48.101967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:49.858551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:51.850668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:53.355216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:54.953286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:56.603595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:58.569526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:00.180385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:01.775301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:03.593292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:05.548528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:07.095068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:08.981824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:11.039046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:13.060632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:14.808426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:44.813528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:46.576867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:48.187821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:49.946094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:51.938034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:53.437246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:55.038454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:56.696246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:58.661512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:00.261740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:01.863600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:03.684105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:05.626426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:07.180202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:09.072356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:11.152229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:13.149955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:14.895010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:44.895160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:46.665357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:48.279951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:50.041253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:52.027251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:53.526397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:55.124192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:56.876801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:58.772490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:00.347248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:01.974589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:03.785510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:05.709286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:07.275955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:09.161707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:11.275860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:13.237317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:14.975733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:44.979970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:46.750406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:48.383331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:50.126799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:52.112011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:53.604856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:55.207804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:56.999605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:58.873408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:00.435208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:02.060785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:03.885745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:05.790849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:07.368850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:09.252807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:11.397422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:13.349301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:15.065007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:45.076055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:46.838752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:48.480987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:50.236936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:52.204583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:53.687178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:55.294968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:57.144835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:19:58.968198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:00.523131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:02.152634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:03.976537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:05.874347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:07.459589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:09.351411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:11.510738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-15T23:20:13.468136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-15T23:20:22.759017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
bus_widthcache_off_idcache_on_idclockcode_name_iddie_photo_iddie_sizehw_ncoreshw_nthreadspercoreidmanufacturer_idmax_clockmicroarchitecture_idprocessor_family_idtdptechnology_idtransistorsvdd_highvdd_low
bus_width1.0000.1320.4390.126-0.0030.0720.2290.1190.1020.035-0.1060.005-0.2010.0050.2420.2290.137-0.253-0.224
cache_off_id0.1321.0000.199-0.3350.196-0.1580.134-0.2590.035-0.004-0.202-0.031-0.0230.283-0.038-0.122-0.2610.0620.269
cache_on_id0.4390.1991.000-0.195-0.053-0.0120.164-0.2530.214-0.072-0.181-0.0940.0230.1630.1240.269-0.009-0.2210.016
clock0.126-0.335-0.1951.000-0.1110.2000.0470.5330.1500.0870.0650.352-0.093-0.3650.5580.0300.383-0.102-0.487
code_name_id-0.0030.196-0.053-0.1111.000-0.2540.3220.2080.2050.538-0.1680.092-0.0670.324-0.027-0.2080.0180.4450.428
die_photo_id0.072-0.158-0.0120.200-0.2541.000-0.1340.1230.086-0.0880.2910.0250.199-0.1470.0470.2920.201-0.167-0.278
die_size0.2290.1340.1640.0470.322-0.1341.0000.2940.1240.193-0.1260.077-0.0620.0620.381-0.0880.3490.1840.089
hw_ncores0.119-0.259-0.2530.5330.2080.1230.2941.0000.1760.2380.1300.161-0.075-0.3120.4280.0330.5650.037-0.379
hw_nthreadspercore0.1020.0350.2140.1500.2050.0860.1240.1761.0000.1950.1910.2510.1600.2230.3350.1940.2610.0000.122
id0.035-0.004-0.0720.0870.538-0.0880.1930.2380.1951.0000.1620.1120.1060.1420.0290.1720.0890.2800.169
manufacturer_id-0.106-0.202-0.1810.065-0.1680.291-0.1260.1300.1910.1621.0000.0330.451-0.220-0.0980.6250.012-0.082-0.078
max_clock0.005-0.031-0.0940.3520.0920.0250.0770.1610.2510.1120.0331.0000.038-0.0130.1800.0140.0630.0800.000
microarchitecture_id-0.201-0.0230.023-0.093-0.0670.199-0.062-0.0750.1600.1060.4510.0381.000-0.026-0.1040.426-0.0640.0550.093
processor_family_id0.0050.2830.163-0.3650.324-0.1470.062-0.3120.2230.142-0.220-0.013-0.0261.000-0.235-0.176-0.1680.1800.411
tdp0.242-0.0380.1240.558-0.0270.0470.3810.4280.3350.029-0.0980.180-0.104-0.2351.000-0.0280.292-0.112-0.339
technology_id0.229-0.1220.2690.030-0.2080.292-0.0880.0330.1940.1720.6250.0140.426-0.176-0.0281.0000.043-0.169-0.144
transistors0.137-0.261-0.0090.3830.0180.2010.3490.5650.2610.0890.0120.063-0.064-0.1680.2920.0431.0000.098-0.334
vdd_high-0.2530.062-0.221-0.1020.445-0.1670.1840.0370.0000.280-0.0820.0800.0550.180-0.112-0.1690.0981.0000.653
vdd_low-0.2240.2690.016-0.4870.428-0.2780.089-0.3790.1220.169-0.0780.0000.0930.411-0.339-0.144-0.3340.6531.000

Missing values

2024-10-15T23:20:15.206940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-15T23:20:15.480743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idcreated_atupdated_atmanufacturer_idprocessor_family_idmicroarchitecture_idcode_name_idtechnology_idcache_on_idcache_off_iddie_photo_idmodeldateclockmax_clockhw_nthreadspercorehw_ncorestdpsourcebus_widthtransistorsdie_sizevdd_lowvdd_high
012014-11-17 00:01:26.7084532014-11-17 00:01:26.7084539136.3079851.048.4598161236.7231833.60Eunknown date3600.03197.4342631.01.0110.0http://ark.intel.com/products/27089/64-bit-Intel-Xeon-Processor-3_60E-GHz-2M-Cache-800-MHz-FSB56.387569169.0135.01.28751.3875
122014-11-17 00:01:26.7320992014-11-17 00:01:26.7320999136.3079851.048.4598161236.7231833.6unknown date3600.03197.4342632.01.0110.0http://ark.intel.com/products/28019/64-bit-Intel-Xeon-Processor-3_60-GHz-2M-Cache-800-MHz-FSB56.387569169.0135.01.25001.3880
232014-11-17 00:01:26.7543402014-11-17 00:01:26.7543409136.3079851.048.4598161236.7231833.80Eunknown date3800.03197.4342631.01.0110.0http://ark.intel.com/products/27092/64-bit-Intel-Xeon-Processor-3_80E-GHz-2M-Cache-800-MHz-FSB56.387569169.0135.01.28751.3875
342014-11-17 00:01:26.7969282014-11-17 00:01:26.7969289136.3079852.048.4598163236.723183unknown modelunknown date3660.03197.4342632.01.0110.0http://ark.intel.com/products/27103/64-bit-Intel-Xeon-Processor-3_66-GHz-1M-Cache-667-MHz-FSB56.387569125.0112.01.28751.4000
452014-11-17 00:01:26.8473822014-11-17 00:01:26.8473829236.3079853.048.4598164236.723183310unknown date2130.03197.4342631.01.073.0http://ark.intel.com/products/27104/Intel-Celeron-D-Processor-310-(256K-Cache-2_13-GHz-533-MHz-FSB)56.387569125.0112.01.25001.4000
562014-11-17 00:01:26.8685302014-11-17 00:01:26.8685309236.3079853.048.4598164236.723183315unknown date2260.03197.4342631.01.073.0http://ark.intel.com/products/27105/Intel-Celeron-D-Processor-315-(256K-Cache-2_26-GHz-533-MHz-FSB)56.387569125.0112.01.25001.4000
672014-11-17 00:01:26.8886722014-11-17 00:01:26.8886729236.3079853.048.4598164236.723183315Junknown date2260.03197.4342631.01.073.0http://ark.intel.com/products/27106/Intel-Celeron-D-Processor-315315J-(256K-Cache-2_26-GHz-533-MHz-FSB)56.387569125.0112.01.25001.4000
782014-11-17 00:01:26.9083132014-11-17 00:01:26.9083139236.3079853.048.4598164236.723183320unknown date2400.03197.4342631.01.073.0http://ark.intel.com/products/27107/Intel-Celeron-D-Processor-320-(256K-Cache-2_40-GHz-533-MHz-FSB)56.387569125.0112.01.25001.4000
892014-11-17 00:01:26.9290462014-11-17 00:01:26.9290469236.3079853.048.4598164236.723183325unknown date2530.03197.4342631.01.073.0http://ark.intel.com/products/27108/Intel-Celeron-D-Processor-325-(256K-Cache-2_53-GHz-533-MHz-FSB)56.387569125.0112.01.25001.4000
9102014-11-17 00:01:26.9512612014-11-17 00:01:26.9512619236.3079853.048.4598164236.723183325Junknown date2530.03197.4342631.01.084.0http://ark.intel.com/products/27110/Intel-Celeron-D-Processor-325J-(256K-Cache-2_53-GHz-533-MHz-FSB)56.387569125.0112.01.25001.4000
idcreated_atupdated_atmanufacturer_idprocessor_family_idmicroarchitecture_idcode_name_idtechnology_idcache_on_idcache_off_iddie_photo_idmodeldateclockmax_clockhw_nthreadspercorehw_ncorestdpsourcebus_widthtransistorsdie_sizevdd_lowvdd_high
209420952014-11-17 00:02:40.7597102014-11-17 00:02:40.759710919236.307985204.048.4598162236.7231835Y102014-01-07800.0000002000.0000002.02.04.500000http://ark.intel.com/products/83610/Intel-Core-M-5Y10-Processor-4M-Cache-up-to-2_00-GHz56.387569315.576454175.6918971.47867613.51261
209520962014-11-17 00:02:40.7890962014-11-17 00:02:40.789096919236.307985204.048.4598162236.7231835Y10a2014-01-07800.0000002000.0000002.02.04.500000http://ark.intel.com/products/83611/Intel-Core-M-5Y10a-Processor-4M-Cache-up-to-2_00-GHz56.387569315.576454175.6918971.47867613.51261
209620972014-11-17 00:02:40.8193102014-11-17 00:02:40.819310919236.307985204.048.4598162236.7231835Y702014-01-071100.0000002600.0000002.02.04.500000http://ark.intel.com/products/83612/Intel-Core-M-5Y70-Processor-4M-Cache-up-to-2_60-GHz56.387569315.576454175.6918971.47867613.51261
209720982014-11-17 00:02:40.8699602014-11-17 00:02:40.869960919336.307985205.048.4598162236.723183DSP2014-01-07750.0000003197.4342631.04.061.355546http://ark.intel.com/products/83850/Intel-Transcede-T315056.387569315.576454175.6918971.47867613.51261
209820992014-11-17 00:02:40.8988152014-11-17 00:02:40.89881591836.307985202.048.4598162236.723183Z35302014-01-041975.6101343197.4342631.04.061.355546http://ark.intel.com/products/84072/Intel-Atom-Processor-Z3530-2M-Cache-up-to-1_33-GHz56.387569315.576454175.6918971.4786760.00000
209921002014-11-17 00:02:40.9272662014-11-17 00:02:40.92726691836.307985202.048.4598162236.723183Z35702014-01-071975.6101343197.4342631.04.061.355546http://ark.intel.com/products/84324/Intel-Atom-Processor-Z3570-2M-Cache-up-to-2_00-GHz56.387569315.576454175.6918971.4786760.00000
210021012014-11-17 00:02:40.9528922014-11-17 00:02:40.952892919236.307985204.048.4598162236.7231835Y312014-01-10900.0000002400.0000002.02.04.500000http://ark.intel.com/products/84666/Intel-Core-M-5Y31-Processor-4M-Cache-up-to-2_40-GHz56.387569315.576454175.6918971.47867613.51261
210121022014-11-17 00:02:40.9805652014-11-17 00:02:40.980565919236.307985204.048.4598162236.7231835Y512014-01-101100.0000002600.0000002.02.04.500000http://ark.intel.com/products/84669/Intel-Core-M-5Y51-Processor-4M-Cache-up-to-2_60-GHz56.387569315.576454175.6918971.47867613.51261
210221032014-11-17 00:02:41.0022152014-11-17 00:02:41.002215919236.307985204.048.4598162236.7231835Y712014-01-101200.0000002900.0000002.02.04.500000http://ark.intel.com/products/84672/Intel-Core-M-5Y71-Processor-4M-Cache-up-to-2_90-GHz56.387569315.576454175.6918971.47867613.51261
210321042014-11-17 00:02:41.0294082014-11-17 00:02:41.029408919236.307985204.048.4598162236.7231835Y10c2014-01-10800.0000002000.0000002.02.04.500000http://ark.intel.com/products/85234/Intel-Core-M-5Y10c-Processor-4M-Cache-up-to-2_00-GHz56.387569315.576454175.6918971.47867613.51261